ArticleFrontiers in computational neuroscience2015
Unsupervised learning of digit recognition using spike-timing-dependent plasticity.
Article in Frontiers in computational neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 209 papers.
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Who cites it
209 citing papers in PubMed.
- Towards neuromorphic neurotechnologies: integrating brain-inspired computing with brain-computer interfaces.npj biomedical innovations · 2026Review
- Neuromorphic Cardiac Sensing: A Bio-Inspired Spiking Neural Network with Sensory-Adaptive Encoding for Energy-Efficient Arrhythmia Detection from ECG and PPG Signals.Biomimetics (Basel, Switzerland) · 2026Article
- Anion-Engineered Organic Electrochemical Transistors With Multi-Timescale Synaptic Dynamics for Task-Adaptive Spiking Neural Networks.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- System-level FPGA validation of a trainable and robust multiplier-free spiking neural network.iScience · 2026Article
- Synaptic Plasticity as a Function of the Temporal Derivative.bioRxiv : the preprint server for biology · 2026Article
- Organic Transistor-Based Neuromorphic Electronics and Their Recent Applications.Small methods · 2026Review
- Cellular and subcellular specialization enables biology-constrained deep learning.Cell reports · 2026Article
- Article
- Multi-plasticity synergy with adaptive mechanism assignment for training spiking neural networks.Frontiers in neuroscience · 2026Article
- Energy-efficient traffic sign recognition using directly trained spiking neural networks and population decoding.Frontiers in neuroscience · 2026Article
- Adaptive and lightweight surrogate gradients: enhancing training efficiency of spiking neural networks.Frontiers in neuroscience · 2026Article
- Microelectrode arrays cultured with in vitro neural networks for motion control tasks: encoding and decoding progress and advances.Microsystems & nanoengineering · 2025Review
- A frugal Spiking Neural Network for unsupervised multivariate temporal pattern classification and multichannel spike sorting.Nature communications · 2025Article
- Bioinspired high-order in-sensor spatiotemporal enhancement in van der Waals optoelectronic neuromorphic electronics.Nature communications · 2025Article
- Unsupervised post-training learning in spiking neural networks.Scientific reports · 2025Article
- A multiscale brain emulation-based artificial intelligence framework for dynamic environments.Scientific reports · 2025Article
- Chaotic dynamics analysis and digital hardware design of the Izhikevich neuron model.Scientific reports · 2025Article
- Reconfigurable neuromorphic functions in antiferroelectric transistors through coupled polarization switching and charge trapping dynamics.Nature communications · 2025Article
- Finger Vein Recognition Based on Unsupervised Spiking Convolutional Neural Network with Adaptive Firing Threshold.Sensors (Basel, Switzerland) · 2025Article
- Encrypted Spiking Neural Networks Based on Adaptive Differential Privacy Mechanism.Entropy (Basel, Switzerland) · 2025Article
149 more citing papers are in PubMed but not listed here.
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2 authors.
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Abstract
In order to understand how the mammalian neocortex is performing computations, two things are necessary; we need to have a good understanding of the available neuronal processing units and mechanisms, and we need to gain a better understanding of how those mechanisms are combined to build functioning systems. Therefore, in recent years there is an increasing interest in how spiking neural networks (SNN) can be used to perform complex computations or solve pattern recognition tasks. However, it remains a challenging task to design SNNs which use biologically plausible mechanisms (especially for learning new patterns), since most such SNN architectures rely on training in a rate-based network and subsequent conversion to a SNN. We present a SNN for digit recognition which is based on mechanisms with increased biological plausibility, i.e., conductance-based instead of current-based synapses, spike-timing-dependent plasticity with time-dependent weight change, lateral inhibition, and an adaptive spiking threshold. Unlike most other systems, we do not use a teaching signal and do not present any class labels to the network. Using this unsupervised learning scheme, our architecture achieves 95% accuracy on the MNIST benchmark, which is better than previous SNN implementations without supervision. The fact that we used no domain-specific knowledge points toward the general applicability of our network design. Also, the performance of our network scales well with the number of neurons used and shows similar performance for four different learning rules, indicating robustness of the full combination of mechanisms, which suggests applicability in heterogeneous biological neural networks.
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